The present invention relates to selecting a region of interest for use in extracting useful information from one or more images.
Identification of the most affected regions relating to physiological changes in a human organ can help in capturing the dynamics of the underlying pathology. For example, it is known that the articular cartilage undergoes morphometric changes during osteoarthritis (Eckstein et al., 2006). Characterization of the regions that best discriminate the healthy and the diseased can be used to reduce the sample size in clinical studies which is desirable since this translates into reduced costs and less patient burden. By studying the important regions it may also be possible to find clues about the pathophysiology of the disease.
Studies involving region of interest (ROI) analysis in human organs have been carried out by various researchers. For instance, the distribution of morphometric changes in the brain caused by genetic, environmental factors or various neurodegenerative diseases has been investigated extensively (Andreasen et al., 1994; Dickerson et al., 2001; Pruessner et al., 2000; Raz et al., 1998; Xu et al., 2000). Similar methods have been developed to analyze the articular cartilage. Hohe et al. observed signal intensity differences in pre-defined sub-regions of the patellar cartilage (Hohe et al., 2002). In a recent study, Wirth and Eckstein (Wirth and Eckstein, 2008) measured regional cartilage thickness in pre-defined anatomically based ROIs. In a longitudinal study, Blumenkrantz et al 2004. quantified changes in structural parameters of bone and cartilage by manually segmenting them in four anatomically meaningful sub-compartments (Blumenkrantz et al., 2004).
The majority of these studies rely on predefined ROIs. Specifically for brain atrophy measurements, ROI-based analysis is the current gold standard (Good et al., 2002). However, due to the manual segmentation task, these methods are labor-intensive and therefore typically focus on a limited number of ROIs. The time and cost involved in the manual segmentation task also makes it difficult to compare large subject groups. Another important problem is that often it is not obvious how to define the subregions that are optimal for the ROI analysis meaning that inter/intra observer reliability might be low.
In order to detect structural anomalies and other pathological differences reliably and accurately in an unbiased way, new techniques have been developed (Freeborough and Fox, 1998; Yushkevich et al., 2003). Voxel-Based Morphometry (VBM) is one such method. Proposed by Ashburner and Friston (Ashburner and Friston, 2000), VBM is increasingly being used to investigate differences in brain morphology between patient and control groups. VBM is widely being used as a tool to examine changes in brain morphometry during healthy aging (Good et al., 2001) or for various neurological conditions including Alzheimer's disease, and Semantic Dementia (Baron et al., 2001; Mummery et al., 2000). The output of the method is a probabilistic map which indicates regions of significant gray matter or white matter concentration differences. This map is usually computed using a statistical technique called statistical parametric mapping where parametric statistical models are assumed at each voxel. Holmes (1994) and Nichols and Holmes (2002) has suggested a alternative nonparametric method based on permutation test theory which they show is a viable alternative when the assumptions required for a parametric approach are not met.
Similar to the aim of VBM analysis, Dam et al. (Dam et al., 2006) computed 2D thickness maps of focal articular cartilage loss from knee MRI. and performed focal statistical tests to illustrate the local discriminative power of cartilage thickness measurements. This was done by a t-test at each position independently, and no attempt to reach a global discriminative (soft) region map was reported. These techniques, however, are based on voxel-by-voxel statistical comparisons where it is assumed that each voxel represents the same anatomical position across all the images, and Bookstein (Bookstein, 2001) pointed out that imperfect registration might lead to interpreting the results as a characteristic of the disease, while in fact this effect might be caused by misalignment of the images. Although smoothing can help alleviate mis-registration, the fundamental voxel correspondence problem still remains challenging. Moreover, the voxel-wise analysis ignores the neighborhood of a voxel which may underscore the anatomical relationship of a voxel.
Previously, we reported preliminary results on identification of regions of pathological differences in the articular cartilage (Qazi et al., 2008; Qazi et al., 2007a). Both methods employ a computationally intensive bootstrapping technique, based on voxel-wise analysis, to identify an ROI, which is further regularized by curve evolution methods. A drawback of these methods is that they do not consider prior information, such as the anatomical neighbourhood relationship of a particular voxel. Without prior information, the ROI problem is combinatorial and therefore, finding an optimal solution requires an exhaustive search, which makes the problem computationally intractable.